A laparoscopic surgery is one of the procedures commonly used in modern healthcare, which is significant for treating patients. However, a great number of novice physicians need extensive training in order to be skilled in performing the procedure. At present, most surgical trainings in domestic hospitals are carried out using a substitution, the use of which has shortcomings such as inaccuracy and limited supplies. With continuous development of computer technologies, the virtual reality technology is gradually coming out, but also presents a new approach for surgery trainings, e.g. the virtual laparoscopic surgery.
In practice, the surgery involves various operations, such as touching, cutting, hemostasis, suture and the like. During the virtual surgery, it is desirable to replicate as many of these interactions as possible. Generally, establishment of a virtual surgery includes modeling, deformation driving, cutting, suture and the like. For organs at different locations, additional realistic features may be included according to their characteristics, such as blood, vascular vessels and the like. A volume model will be modeled for an organ subsequent to acquisition of its skin model by scanning. Common volume models include tetrahedral model, hexahedral model, metaball model and particle model. The driving involves addition of a physical or geometric driving method to the model, and commonly-used methods include mass spring method, finite element method and meshless method. The cutting and suture relate to handling changes in topology.
In the existing cutting method, most of the models used are the tetrahedral model or the hexahedral model. The tetrahedral model will produce more accurate splitting edges during cutting, but has a huge calculation, likely leading to an abnormal tetrahedron and more trouble for subsequent works. The hexahedral model determines the cutting edges through constant subdivisions, which require more smoothing operations. The conventional cutting methods based on the metaball model generally perform subdivision of metaballs by way of the octree, which tends to produce excessive small metaballs after the subdivision, and significantly increases boundary conditions while reducing the iterative speed, leading to difficulties for processing.